Understanding spoken language through TalkBank

MacWhinney, Brian

doi:10.3758/s13428-018-1174-9

Cited by 29 publications

(24 citation statements)

References 38 publications

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“…It consists of picture descriptions elicited by the Cookie Theft Picture, generated by healthy participants and patients with probable AD, and linked to their neuropsychological data (i.e., MMSE). It was collected by the University of Pittsburgh [ 51 ] and distributed through DementiaBank [ 52 ]. BEA Hungarian Dataset: This is a phonetic database, containing over 250 hours of multipurpose Hungarian spontaneous speech.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

137

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Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

137

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“…The Pitt Corpus (Becker et al, 1994 ), which forms part of the DementiaBank (MacWhinney, 2019 ), and more specifically its Cookie Theft test sub-corpus, remains one of the very few available datasets to link spontaneous speech from dementia patients and healthy controls (recordings and transcriptions) with clinical information. Therefore, this dataset has been used in several studies, including the studies by Fraser et al ( 2016 ); Hernández-Domínguez et al ( 2018 ), and others (Yancheva and Rudzicz, 2016 ; Luz, 2017 ; Orimaye et al, 2017 ; Guo et al, 2019 ; Mirheidari et al, 2019b ; Haider et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

“…This study uses the ADReSS subset of the Pitt Corpus, derived from a dataset gathered longitudinally between 1983 and 1988 on a yearly basis as part of the Alzheimer Research Program at the University of Pittsburgh (Becker et al, 1994 ; Corey Bloom and Fleisher, 2000 ), and made available through DementiaBank (MacWhinney, 2019 ). Participants are categorised into three groups: dementia, control (non-AD), and unknown status.…”

Section: Datasetmentioning

confidence: 99%

“…The manual transcripts in CHAT format (MacWhinney, 2019 ) were first converted into word and token sequences which represent what was actually produced in speech. For instance, the annotations ‘w [x n]’, which indicate that the word ‘w’ was repeated n times were replaced by n repetitions of w, punctuation marks and various comments annotated between‘[]’ were removed.…”

Section: Ad Detectionmentioning

confidence: 99%

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Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech

Martinc

Haider

Pollak

et al. 2021

Front. Aging Neurosci.

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Background: Advances in machine learning (ML) technology have opened new avenues for detection and monitoring of cognitive decline. In this study, a multimodal approach to Alzheimer's dementia detection based on the patient's spontaneous speech is presented. This approach was tested on a standard, publicly available Alzheimer's speech dataset for comparability. The data comprise voice samples from 156 participants (1:1 ratio of Alzheimer's to control), matched by age and gender.Materials and Methods: A recently developed Active Data Representation (ADR) technique for voice processing was employed as a framework for fusion of acoustic and textual features at sentence and word level. Temporal aspects of textual features were investigated in conjunction with acoustic features in order to shed light on the temporal interplay between paralinguistic (acoustic) and linguistic (textual) aspects of Alzheimer's speech. Combinations between several configurations of ADR features and more traditional bag-of-n-grams approaches were used in an ensemble of classifiers built and evaluated on a standardised dataset containing recorded speech of scene descriptions and textual transcripts.Results: Employing only semantic bag-of-n-grams features, an accuracy of 89.58% was achieved in distinguishing between Alzheimer's patients and healthy controls. Adding temporal and structural information by combining bag-of-n-grams features with ADR audio/textual features, the accuracy could be improved to 91.67% on the test set. An accuracy of 93.75% was achieved through late fusion of the three best feature configurations, which corresponds to a 4.7% improvement over the best result reported in the literature for this dataset.Conclusion: The proposed combination of ADR audio and textual features is capable of successfully modelling temporal aspects of the data. The machine learning approach toward dementia detection achieves best performance when ADR features are combined with strong semantic bag-of-n-grams features. This combination leads to state-of-the-art performance on the AD classification task.

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“…Our dataset is made up of transcripts from the DementiaBank corpus. The DementiaBank corpus is a set of recordings of cognitive tests, which forms part of the larger TalkBank project (MacWhinney, 2019). The subset of DementiaBank used in this study encompasses recordings and their corresponding transcriptions, where patients with Alzheimer's and controls describe a picture known as the "Cookie Theft" scene, taken from the Boston Diagnostic Aphasia Examination (Becker et al (1994)).…”

Section: Software and Datamentioning

confidence: 99%

Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

Millington

Luz

2021

Front. Comput. Sci.

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In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.

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Understanding spoken language through TalkBank

Cited by 29 publications

References 38 publications

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech

Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

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